Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data
暂无分享,去创建一个
Ralf Eggeling | Michael Scherer | Nico Pfeifer | Adrin Jalali | Lisa Handl | Adrin Jalali | N. Pfeifer | M. Scherer | L. Handl | R. Eggeling
[1] Thomas Lengauer,et al. Comprehensive Analysis of DNA Methylation Data with RnBeads , 2014, Nature Methods.
[2] Takaya Saito,et al. Target gene expression levels and competition between transfected and endogenous microRNAs are strong confounding factors in microRNA high-throughput experiments , 2012, Silence.
[3] Nico Pfeifer,et al. Interpretable Per Case Weighted Ensemble Method for Cancer Associations , 2014, WABI.
[4] David M. Simcha,et al. Tackling the widespread and critical impact of batch effects in high-throughput data , 2010, Nature Reviews Genetics.
[5] Andrew E. Teschendorff,et al. Cell and tissue type independent age-associated DNA methylation changes are not rare but common , 2018 .
[6] David Modiano,et al. Resistance to malaria through structural variation of red blood cell invasion receptors , 2016, Science.
[7] Nicola J. Rinaldi,et al. Genetic effects on gene expression across human tissues , 2017, Nature.
[8] Sean R. Collins,et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.
[9] Rémi Emonet,et al. Landmarks-based kernelized subspace alignment for unsupervised domain adaptation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] B. Stranger,et al. Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics , 2011, Genetics.
[11] O. Stegle,et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2016, Genome Biology.
[12] Alex E. Lash,et al. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..
[13] Philipp Khaitovich,et al. Aging and Gene Expression in the Primate Brain , 2005, PLoS biology.
[14] M. Gerstein,et al. A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data , 2003, Science.
[15] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[16] Mihaela van der Schaar,et al. A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics , 2016, NIPS.
[17] Robin M. Murray,et al. Epigenome-Wide Scans Identify Differentially Methylated Regions for Age and Age-Related Phenotypes in a Healthy Ageing Population , 2012, PLoS genetics.
[18] Mehryar Mohri,et al. Domain Adaptation in Regression , 2011, ALT.
[19] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[20] Hermann Brenner,et al. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. , 2014, Human molecular genetics.
[21] Yuan Shi,et al. Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[22] H QUASTLER,et al. GENETIC EFFECTS. , 1964, New York state journal of medicine.
[23] Jeffrey T Leek,et al. On the design and analysis of gene expression studies in human populations , 2007, Nature Genetics.
[24] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[25] Alfonso Valencia,et al. Distinct DNA methylomes of newborns and centenarians , 2012, Proceedings of the National Academy of Sciences.
[26] D. Schübeler. Function and information content of DNA methylation , 2015, Nature.
[27] Joshua M. Stuart,et al. The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.
[28] B. Christensen,et al. Aging and Environmental Exposures Alter Tissue-Specific DNA Methylation Dependent upon CpG Island Context , 2009, PLoS genetics.
[29] Timothy E. Reddy,et al. Dynamic DNA methylation across diverse human cell lines and tissues , 2013, Genome research.
[30] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[31] Richard M Myers,et al. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape , 2013, Genome Biology.
[32] Marcel H. Schulz,et al. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction , 2016, bioRxiv.
[33] Francesco Marabita,et al. A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data , 2012, Bioinform..
[34] M. Daly,et al. Genetic and Epigenetic Fine-Mapping of Causal Autoimmune Disease Variants , 2014, Nature.
[35] Herbert A. Sturges,et al. The Choice of a Class Interval , 1926 .
[36] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[37] Aviv Regev,et al. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. , 2015, Annual review of cell and developmental biology.
[38] T. Ideker,et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. , 2013, Molecular cell.
[39] A. Gnirke,et al. Charting a dynamic DNA methylation landscape of the human genome , 2013, Nature.
[40] A. E. Hoerl,et al. Ridge regression: biased estimation for nonorthogonal problems , 2000 .
[41] Kristen Grauman,et al. Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.
[42] Bhanukiran Vinzamuri,et al. Constrained elastic net based knowledge transfer for healthcare information exchange , 2014, Data Mining and Knowledge Discovery.
[43] S. Horvath. DNA methylation age of human tissues and cell types , 2013, Genome Biology.
[44] Ole Winther,et al. DeepLoc: prediction of protein subcellular localization using deep learning , 2017, Bioinform..
[45] Thomas Lengauer,et al. Innovations: Bioinformatics-assisted anti-HIV therapy , 2006, Nature Reviews Microbiology.
[46] Rama Chellappa,et al. Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.
[47] Fabio Gagliardi Cozman,et al. Random Generation of Bayesian Networks , 2002, SBIA.
[48] S. Ramaswamy,et al. Systematic identification of genomic markers of drug sensitivity in cancer cells , 2012, Nature.
[49] Michael I. Jordan,et al. Unsupervised Domain Adaptation with Residual Transfer Networks , 2016, NIPS.
[50] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[51] Andrew E. Teschendorff,et al. Age-associated epigenetic drift: implications, and a case of epigenetic thrift? , 2013, Human molecular genetics.
[52] Christian Wachinger,et al. Domain adaptation for Alzheimer's disease diagnostics , 2016, NeuroImage.
[53] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[54] Atul J Butte,et al. Robust meta-analysis of gene expression using the elastic net , 2015, Nucleic acids research.